A simple approach to target identification through gene expression studies has been to cluster the expression profiles and look for coregulated genes within clusters. Within systems biology mechanistic models of gene expression are typically constructed through differential equations. mRNA’s production is taken to be proportional to transcription factor activity (with the proportionality given by the sensitivity) and the mRNA is assumed to decay at a particular rate. The assumption that coregulated genes have similar profiles is equivalent to assuming both the decay and the sensitivity are high. Typically researchers either use a data driven approach (such as clustering) or a model based approach (such as differential equations). In this talk we advocate hybrid techniques which have aspects of the mechanistic and data driven models. We combine simple differential equation models with Gaussian process priors to make probabilistic models with mechanistic underpinnings. We show applications in target identification from mRNA measurements.